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Sync data from
Amazon Athena to PlanetScale

Connect your data from Amazon Athena to PlanetScale with Hightouch. No APIs, no months-long implementations, and no CSV files. Just your data synced forever.

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Amazon Athena.
Hightouch sync.

Trusted by data teams at

Trusted by data teams at

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Integrate your data in 3 easy steps

  1. 01

    Add your source and destination

    Connect to 15+ data sources, like Amazon Athena, and 150+ destinations, like PlanetScale.

    Amazon Athena.


    Connector beam.

    Log in

  2. 02

    Define your model

    Use SQL or select an existing dbt or Looker model.

    Define your model
  3. 03

    Sync your data

    Define how fields from your model map to PlanetScale, and start syncing.

    Amazon Athena.
    Connector beam.
    Amazon Athena.


    Connector beam.


    Amazon Athena.


    Connector beam.


    Amazon Athena.


    Connector beam.


    Amazon Athena.


    Connector beam.


Model your Amazon Athena data using any of these methods

  • dbt Model Selector

    Semi-opaque, open dropdown with three example dbt model names such as ''.

    Sync directly with your dbt models saved in a git.

  • Looker

    Semi-opaque open dropdown with three example Look names such as 'look.1'.

    Query using looks. Hightouch turns your look into SQL and will pull from your source.

  • SQL Editor

    Empty SQL editor.

    Create and Edit SQL from your browser. Hightouch supports SQL native to Amazon Athena.

  • Table Selector

    Semi-opaque open dropdown with three example table names such as ''.

    Select available tables and sheets from Amazon Athena and sync using existing views without having to write SQL.

  • Customer Studio

    Visual query builder reading 'All rows that...' with a button labeled 'select a property'.

    For less technical users, pass traits and audiences from Amazon Athena using our visual segmentation builder.

Why is it valuable to sync Amazon Athena data to PlanetScale?

Platforms like Amazon Athena have become the standard for modeling and transforming large quantities of data so you can answer complex analytics questions as quickly and efficiently as possible.

On the other hand, production databases like PlanetScale aren't designed to tackle large complex queries or transform your data. They're built to power your product experiences, and handle large volumes of small transactions in real-time, whether it's looking up or editing information about a single user, processing orders, accepting payments, or even granting access to specific product features.

Unless you have an unlimited amount of time and money, it's impossible to calculate core metrics about your customers and build behavioral prediction models in your PlanetScale instance. Transactional databases just aren't made for analytics use cases. Most likely you're already ingesting all of your production level data into your data warehouse via an ETL pipeline, so the logical step is simply to sync that data to PlanetScale.

Providing user-level recommendations to improve your on-site personalization requires you to categorize your users into groups based on their behavior. You might want to offer a coupon to customers with certain products in their cart, or maybe you want to group users into specific categories (e.g., power users, garden lovers, high-value customers, etc.) Either way, it's only possible and performant to build these data models in your warehouse.

Embedded analytics is also another extremely relevant use case for PlanetScale. If your app offers built-in reporting and visualization features it's much easier to do aggregations and transformations in your warehouse and sync those results to your application database to power your user-facing visualizations.

If you've ever built an internal application like an in-house CRM or a marketing platform, there's a good chance it's running off of your application database. In many cases, your PlanetScale database doesn't have all of the modeled data you need to power this end tool and that's why it's so important to hydrate your PlanetScale instance with modeled customer data directly from your warehouse.

Why should you use reverse ETL to connect Amazon Athena and PlanetScale data?

In the past syncing data from your data warehouse to PlanetScale required you to integrate with various APIs and build and maintain in-house pipelines. Even if your engineering team successfully builds a custom pipeline to your production database, a single API change or rate limit can quickly break everything.

Integrating with third-party APIs is complex, expensive, and time-consuming, so the path of least resistance is often downloading and uploading manual CSV files. CSVs are not a long-term solution because they go stale quickly, and you can never fully trust the accuracy of your data.

Workflow automation tools have arisen to solve this problem, but managing various if/then statements creates an intricate web of dependencies prone to failure. On the other hand, customer data platforms (CDPs) force you to create a second source of truth and pay for another storage layer in addition to your warehouse.

Hightouch eliminates these problems with Reverse ETL. You can query directly against your data warehouse using standard SQL, your current tables, and even use your existing data models. All your data is automatically diffed between sync runs to ensure you're only syncing the freshest data.

Any failed rows are automatically retried in the next sync. You can easily view live API responses/requests and use a live debugger to identify failed runs and problematic data. Hightouch will write the results of your sync back to your warehouse so you can easily analyze your logs.

Hightouch even integrates with git so you can manage and update your syncs bi-directionally in your git repo. You can even send alerts to your favorite messaging tools like Slack or email.

With Hightouch, all you have to do is connect to your data warehouse and map the proper columns in your data warehouse to the appropriate fields in your destination.

Run complex queries on your data source and copy the results into a PlanetScale table.

Exercise granular control over batch sizes and more.

Amazon Athena.

About Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Learn more about Amazon Athena

About PlanetScale

PlanetScale is a MySQL-compatible serverless database that brings you scale, performance, and reliability — without sacrificing developer experience. With PlanetScale, you get the power of horizontal sharding, non-blocking schema changes, and many more powerful database features without the pain of implementing them.

Learn more about PlanetScale

Other Amazon Athena Integrations

Amazon Athena to Yahoo

Hightouch Playbooks: Best practices to leverage reverse ETL

  • Manage Omnichannel Split Testing across Facebook and SFMC.

    Manage Omnichannel Split Testing across Facebook and SFMC

    This playbook will show you how to manage multivariate testing across Facebook and Salesforce Marketing Cloud with Hightouch Audiences.

  • Create Facebook Lookalike Audiences from High Value Users.

    Create Facebook Lookalike Audiences from High Value Users

    In this playbook, you will learn how to use Hightouch to sync an audience of high-value customers to Facebook to generate lookalike audiences.

  • Sync Nested Object Data to Braze.

    Sync Nested Object Data to Braze

    This playbook will help you understand how and why you should be leveraging nested object arrays in Braze to deeply personalize your omnichannel marketing campaigns.

Read more about Hightouch

  • What is Reverse ETL? The Definitive Guide .

    What is Reverse ETL? The Definitive Guide

    Everything you need to know about Reverse ETL.

  • What is Operational Analytics & Why You Should Use It.

    What is Operational Analytics & Why You Should Use It

    Operational Analytics shifts the focus from simply understanding data to taking action on it in the tools that run business processes. Instead of using dashboards to make decisions, Operational Analytics is focused on turning insights into action – automatically.

  • dbt Cloud: 4 Reasons for Data Teams to Embrace it.

    dbt Cloud: 4 Reasons for Data Teams to Embrace it

    The biggest benefit that dbt Cloud offers to data teams and analytics engineers? Freedom from distractions, and the ability to focus where you can add unique value making sense of your company's data.

Activate data to any of your marketing and advertising tools

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Facebook Custom Audiences
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Austin Hay.
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This might be one of the greatest inventions for technical marketers since the advent of legacy CDPs back in 2015.

Austin Hay

Austin Hay

Head of Marketing Technology


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Your data is always secure

Unlike other SaaS tools, Hightouch never stores any of your data.
SOC 2 Type 2 compliant.

SOC 2 Type 2 compliant

Your data stays secure, available, and confidential. To see our report, .

GDPR compliant.

GDPR compliant

If you’re in the EU, your data is only processed on EU data centers.

HIPAA compliant.

HIPAA compliant

Healthcare companies like ThirtyMadison, Chapter Health, and Headway trust Hightouch.

CCPA compliant.

CCPA compliant

To see our DPA (Data Processing Addendum), .


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